Software Implementation of CCSDS Recommended Hyperspectral Lossless Image Compression

摘要

HyperSpectral Imagers (HySI) are used in the spacecraft or aircrafts to get minute characteristics of target element through capturing image in a large number of narrow and contiguous bands. HySI data represented as data cube with two dimensions representing spatial distribution and third dimension providing band information is huge in volume and challenging task to handle. Hence onboard compression becomes a necessary for optimal usage of onboard storage and downlink bandwidth. CCSDS recommended 123.0-B-1 standard[2] has been released with onboard compression scheme of hyperspectral data. The scheme is based on Fast Lossless algorithm and consists of two main functional blocks namely Predictor and Encoder. Predictor algorithm can be implemented in two modes 'Full Neighborhood Oriented' and 'Reduced Column Oriented'. Encoder algorithm also defines two options 'sample-adaptive' and 'block-adaptive'. We have developed a MATLAB based model implementing the compression scheme with all options defined by the standard. Decompression model is also developed for getting back actual data and end to end verification. Four sets of HySI data (AVIRIS, Hyperion, Chandrayan-1 and FTIS) have been applied as input to the developed model for evaluation of the model. Compression ratio achieved is between 2 to 3 and lossless compression is ensured for each set of data as Mean Square Error (MSE) is zero for all hyperspectral images. Also visual reconstruction of decompressed data matches with original ones. In this paper we have discussed algorithm implementation methodology and results. Reference [1]Consultative Committee for Space Data Systems (CCSDS) [Online]. Available: http://www.ccsds.org. [2]Lossless Multispectral & Hyperspectral Image Compression. Recommendation for Space Data System Standards, CCSDS 123.0-B-1. Blue Book. Issue 1. Washington, D.C.: CCSDS, May 2012. [3]Lossless Data Compression. Recommendation for Space Data System Standards, CCSDS 121.0-B-2. Blue Book. Issue 2. Washington, D.C.: CCSDS, May 2012. [4]Lossless Data Compression. Report Concerning Space Data System Standards, CCSDS 120.0-G-3. Informational Report, Green Book. Washington, D.C.: CCSDS, April 2013. [5]AVIRIS & Hyperion Hyperspectral Images [Online]. Available:http://compression.jpl.nasa.gov/hyperspectral. [6]Khalid Sayood, Introduction to Data Compression, MK Publisher, San Francisco, USA, ISBN 13: 978-0-12-620862-7. [7]Multispectral Hyperspectral Data Compression Working Group.[Online].Available:http://cwe.ccsds.org/sls/default.aspx. [8]G. S. M. Weinberger and G. Sapiro, "The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS," IEEE Trans. Image Process, vol. 9, no. 8, pp.1309–1324, 2000. [9]N. M. X. Wu, "Context-based adaptive, lossless image coding," IEEE Trans. Commun., vol. 45, no. 4, pp. 437–444,1997. [10]X. Wu and N. Memon, "Context-based lossless interband compression-extending calic," IEEE Trans. Image Process., vol. 9, no. 6, pp. 994–1001, 2000. [11]"Context-based adaptive, lossless image coding," IEEE Trans. Commun., vol. 45, no. 4, pp. 437–444, 1997. [12]J. Mielikainen, "Lossless compression of hyperspectral images using lookup tables," IEEE Signal Process. Lett., vol. 13, no. 3, pp. 157–160, Mar. 2006. [13]Y. S. B. Huang, "Lossless compression of hyperspectral imagery via lookup tables with predictor selection," Proc. SPIE, vol. 6365, no. 63650L, 2006. [14]J. Mielikainen and P. Toivanen, "Lossless compression of hyperspectral images using a quantized index to lookup tables," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, pp. 474–478, July 2008. [15]Jose Enrique Sánchez, Estanislau Auge, Josep Santaló, Ian Blanes, Joan Serra-Sagristà, Aaron Kiely, "Review and implementation of the emerging CCSDS Recommended Standard for multispectral and hyperspectral lossless image coding", First International Conference on Data Compression, Communications and Processing, IEEE Computer Society, 2011. [16]D. Keymeulen, N. Aranki, B. Hopson, A. Kiely, M. Klimesh, and K.Benkrid, "GPU Lossless Hyperspectral Data Compression System for Space Applications," 2012 IEEE Aerospace Conference, March 2012. [17]Bormin Huang, Satellite Data Compression, Springer New York Dordrecht Heidelberg London, ISBN 978-1-4614-1182-6. [18]Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Pearson Education, ISBN 978-81-317-2695-2.